Automated Author Profile于, 鑫
于, 鑫
Current S-Index
Sum of Dataset Indices for all datasets
Average Dataset Index per Dataset
Average Dataset Index per dataset
Total Datasets
Total datasets for this author
Average FAIR Score
Average FAIR Score per dataset
Total Citations
Total citations to the author's datasets
Total Mentions
Total mentions of the author's datasets
S-Index Interpretation
The S-Index (Sharing Index) is a comprehensive metric that represents the cumulative impact of all your datasets. It is calculated as the sum of Dataset Index scores across all your claimed datasets.
What it means:
- A higher S-index indicates greater overall impact of your datasets relative to typical datasets in their fields of research
- The S-Index grows as you add more datasets or as existing datasets gain more citations and mentions
- It provides a single number to track your research data impact over time
Current S-Index: 1.4 (sum of 4 datasets Dataset Index scores)
More information here.
S-Index Over Time
Cumulative Citations Over Time
Cumulative Mentions Over Time
Datasets
This dataset contains transcriptomic and metabolomic data from Lilium brownii var. viridulum bulbs under 2,4-Di-tert-butylphenol (2,4-DTBP) treatment, exploring its effects on active constituents. It includes RNA sequencing and LC-MS data from three developmental stages (May–July 2023) for control (LY1CK, LY2CK, LY3CK) and 2 mg·mL⁻¹ 2,4-DTBP-treated (LY1A2, LY2A2, LY3A2) samples, with differential gene expression, metabolite profiles, and physiological data (SOD, CAT, POD, MDA, phenols, flavonoids, saponins, polysaccharides). Correlation analysis links 80 genes to flavonoid metabolites. Supplementary tables and figures support the findings. Data are in FASTQ, mzXML, Excel, and image formats, suitable for studying plant stress responses and secondary metabolite biosynthesis.
Authors
- 于, 鑫
This dataset contains transcriptomic and metabolomic data from Lilium brownii var. viridulum bulbs under 2,4-Di-tert-butylphenol (2,4-DTBP) treatment, exploring its effects on active constituents. It includes RNA sequencing and LC-MS data from three developmental stages (May–July 2023) for control (LY1CK, LY2CK, LY3CK) and 2 mg·mL⁻¹ 2,4-DTBP-treated (LY1A2, LY2A2, LY3A2) samples, with differential gene expression, metabolite profiles, and physiological data (SOD, CAT, POD, MDA, phenols, flavonoids, saponins, polysaccharides). Correlation analysis links 80 genes to flavonoid metabolites. Supplementary tables and figures support the findings. Data are in FASTQ, mzXML, Excel, and image formats, suitable for studying plant stress responses and secondary metabolite biosynthesis.
Authors
- 于, 鑫
This is a set of data obtained from a scientific research experiment and is used to support the inference of the experimental results.
Authors
- 于, 鑫